Expert Systems with Applications: An International Journal

Detection by fitting is a basic task in the fault diagnosis of electric transmission lines. To effectively solve the problem of missed and false detection in multi-fitting detection caused by occlusion between fittings and complex ...

• A latent knowledge reasoning model for fitting detection was proposed.
• Scenario ...

• A novel evaluation framework for storm surge disaster emergency plans is established.

Storm surges are natural disasters that cause abnormal fluctuations in seawater owing to severe atmospheric disturbances. Storm surges have the characteristics of a fierce, fast and destructive force, and can cause serious casualties ...

First-article inspection of flexographic printing labels (FPLs) is significant before the mass production of FPLs, which is manually implemented by quality check (QC) workers in real industries. In this paper, an adaptive coarse-to-...

• A new mathematical model is introduced for electrical capacitance tomography.
• A ...

Low-quality tomograms constrain the potential of the electrical capacitance tomography technology. In order to break through this bottleneck and innovate reconstruction algorithms, the deep transfer learning prior (DTLP) is introduced ...

Crowdsourcing offers the prospect of a more open and socialized approach to product development by bringing together crowdsourcing community resources and connecting open innovation participants. Doing this effectively involves finding ...

Artificial intelligence (AI) that utilizes neural networks (NNs) has a broad range of applications. However, NNs necessitate significant amounts of computation and data storage, which imposes considerable hardware demands and drives ...

• A multi-perspective ECG feature set is constructed.
• Mutual information is ...

The variability of the ECG patterns among patients often exists in real-world application of ECG classification and limits the generalization ability of existing ECG recognition approach. Furthermore, the class imbalance problem among ...

The power supply system, as an indispensable electronic hardware module in most vehicles, needs the highest level of security and reliability to ensure the normal operation of the vehicle. Efficiently identifying any faulty battery at ...

• Proposing a new time series feature extraction and transformation method PVT.
• ...

In order to effectively integrate medical data and alleviate the problem of uneven distribution of medical resources. In this paper, we combine the techniques of expert systems, graph neural networks, and knowledge graphs to propose a ...

• A deep learning-based prediction framework of temperature control time is proposed.

Accurate prediction of temperature control time is essential to improving the stability of wide-thick slab hot rolling production. However, the historical rolling data have high-dimensional and nonlinear characteristics, and there are ...

This paper proposes a new generative model to produce signals of variable lengths. The proposed frequency-learning generative network (FLGN), which is designed and trained based on signal processing knowledge, can generate signals in a ...

Multimodal medical image fusion extracts useful information from different modal medical images and integrates them into one image for a comprehensive and objective lesion description. However, existing methods ignore the simultaneous ...

• Deep reinforcement learning framework called Safety AARL for asset allocation.
• ...

Dynamic asset allocation involves adjusting asset weights based on performance to reduce risks according to market conditions. This paper proposes a novel framework called safety asset allocation reinforcement learning (AARL), which ...

Detecting fine-grained changes among a set of images taken at different times is a challenging problem, which is important for the applications such as high-value scene monitoring and structural inspection. Existing methods use a ...

• A novel fine-grained change detection framework.
• A novel phase-based fine-...

Product defects are a widespread concern for manufacturers when conducting quality and customer relationship management. Prior approaches addressed many electronic products however cell phones are still unexplored. Moreover, prior work ...

Sensor-based human activity recognition (HAR) has drawn extensive attention from the research community due to its potential applications in various domains, including interactive gaming, activity monitoring, healthcare, etc. Although ...

• Light-MHTCN requires minimal preprocessing and no manual feature engineering.
• ...

The high dimensionality of decision variables in large-scale multi-objective optimization problems poses significant challenges for evolutionary algorithms, which often struggle to achieve efficient search, are prone to premature ...

A novel way of performing nondominated sorting in a multiobjective optimization problem is proposed using a modified directional Bat algorithm. Unlike NSGA-II, where the solutions of two generations are merged and then sorted for ...

The cold chain logistics context-aware recommendation containing time, location, environment, activity, user device status and other information has good recommendation accuracy. However, traditional cold chain logistics context-aware ...

Recommender systems suggest relevant item(s) to a new user by analyzing the existing user/item data. In recommender systems, collaborative filtering (CF) is a widely used technique to understand user preferences. The CF technique ...

The reduction of electricity loss and the effective management of electricity demand are vital operations for production and distribution electricity enterprises. To achieve these goals, accurate forecasts of aggregate and individual ...

During multiobjective evolutionary algorithm (MOEA) evolution, the mating and environmental selection operators are crucial in selecting promising individuals and enriching the MOEA’s performance. However, MOEAs must combat obstacles ...

In supervised computer vision tasks, convolutional neural networks (CNNs) have demonstrated superiority over alternative methods. However, training and validating these models requires large-scale labeled datasets, which are generated ...

• Reasonable active learning criteria and an effective GAN-based training strategy.

Deep learning models have been recently proposed in the applications of missing data imputation. In this paper, we review the popular statistical, machine learning, and deep learning approaches, and discuss the advantages and ...

• A comprehensive evaluation of deep learning models for missing data imputation.

Hierarchy is one of the fundamental characteristics of complex systems. A hierarchical inference model for complex networks is an essential mechanism for capturing the hierarchical structure of systems, which is crucial for numerous ...

• Overcome the failure of the original model on real-world weighted networks.
• ...

Local differential privacy federated learning (LDP-FL) is a framework to achieve high local data privacy protection while training the model in a decentralized environment. Currently, LDP-FL’s trainings are suffering from efficiency ...

• Multi-privacy estimator obtains more accurate global gradients than existing ones.

The integrity of industrial data is of great significance to the related technology research in the industrial field. Aiming at the problem of high missing rate of time series data in industrial system, a multi-feature generation ...

Transportation activities, especially road transportation, have a great impact on economic growth. On the other hand, sustainability is a major concern for transportation planning. In this work, a data-oriented network is developed to ...

Nowadays researchers want to be cautious about cost of building models which can generate false positives and false negatives in unexpected ways. They keep on searching for various measures for controlling such behavior depending upon ...